1 Introduction

Type 2 diabetes is a major public health concern in Scotland, with its prevalence risng by approximately 40% in the last decade (D. Colhoun & McKnight, 2020). Effective medication management is important for preventing complications and reducing healthcare burden associated with the condition. Prescribing patterns therefore provide valuable insights into population health, but they are ssensitive to changes in healthcare access, media influence and wider social conditions.

The COVID-19 pandemic introduced unprecedented disruption to daily life. Lockdowns contributed to lifestyle changes associated with increased metabolic risk, while restricted access to healthcare may have affected diagnosis and routine management, which may have impacted existing socioeconomic disparities. Diabetes prevalence is 80% higher in the most deprived fifth of the population than in the fifth most affluent, and the gap is widening (D. Colhoun & McKnight, 2020).

This report examines how prescribing of common Type 2 medications, changed during the COVID-19 pandemic across Scotland. The aim is to assess whether the pandemic altered prescribing patterns, and whether these changes differed by levels of socioeconomic deprivation.

2 Methods

Prescription data were obtained from Public Health Scotland’s Prescriptions in the Community dataset, specifically the “Data by Prescriber Location” records, covering the period from January 2020 to December 2021. Population estimates were drawn from the National Records of Scotland 2022 census release (Table UV302: General Health, Health Board Area, 2019). Socioeconomic deprivation was assessed using the Scottish Index of Multiple Deprivation (SIMD) 2020v2 data zone lookup file. NHS Scotland health board boundaries were incorporated using publicly available shapefiles to enable spatial analysis and visualisation in prescribing across regions.

The data was then filtered for type 2 diabetes medications including biguanides (metformin), SGLT2 inhibitors (gliflozins), sulphonylureas (gliclazide, glimepiride), DDP-4 inhibitors (gliptins), GLP-1 receptor agonists (glutides), thiazolidinediones (glitazone), with all available formulations. These filtered data were then processed to generate visual summaries and descriptive analyses.

For the purpose of this study, the COVID-19 period is defined as March 2020 to June 2021, covering the introduction of national lockdown measures through to the lifting of major covid restrictions.

library(tidyverse)
library(janitor) # cleaning data
library(gt) # to create pretty tables
library(here) # directory structure
library(readxl) # read in excel file
library(sf) # to read in map data
library(plotly) # for interactive plot
library(scales) # to add colour scheme

# Get data from 24-month period from Jan 2020 - Dec 2021
# Create function to read data in:
prescriptions_data <- function() {
  files <- c(paste0(c("jan", "feb","mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"), "2020"),
             paste0(c("jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"), "2021"))
  
  map_dfr(files, ~read_csv(here("data", paste0(.x, ".csv"))) %>%
                   clean_names() %>%
                   mutate(file_name = .x))}

# Read in data
data_for_years <- prescriptions_data()
deprivation_stats <- read_excel(here("data/SIMD.xlsx"))
# Read in and tidy health board population database
population_data <- read_csv(here("data/general_health_census.csv"), skip = 10) %>% 
  filter(!is.na('All people')) %>%  # Removes NA
  rename(hb_name = "General health",
         hb_population = "All people") %>% 
  select(hb_name, hb_population) %>% 
  filter(!str_detect(hb_population, "Cells")) # Removes rows with text in population (to remove copyright info from data)

3 Results

This graph explored the relationship between….

# Filter for diabetes medications
diabetes_summary <- data_for_years %>%
  filter(!is.na(bnf_item_description)) %>% 
  filter(str_detect(bnf_item_description,
                    regex("metformin|gliptin|gliflozin|glutide|glitazone|gliclazide|glimepiride", ignore_case = TRUE))) %>% # Search for common diabetes medications whilst gnoring upper and lower cases
    group_by(paid_date_month, hbt) %>% 
    summarise(paid_quantity = sum(paid_quantity, na.rm = TRUE)) %>% 
    mutate(paid_date_month = ym(paid_date_month)) # Change months from numbers to letters

# Merge all tables together
combined_health_data <- deprivation_stats %>%
  full_join(diabetes_summary, join_by(HBcode == hbt)) %>%
  left_join(population_data, join_by(HBname == hb_name))

# To calculate prescriptions per person
combined_health_data <- combined_health_data %>%
  group_by(HBname, paid_date_month) %>%
  mutate(quantity_per_head = sum(paid_quantity, na.rm = TRUE) / mean(as.numeric(hb_population))) # Calculate prescriptions per person and change hb_population to numeric value instead of character
# Creating a summary table and filtering for common type 2 medications
totalmeds_plot <- combined_health_data %>% 
  ggplot(aes(x = paid_date_month, y = quantity_per_head)) +
  # Add vertical lines for COVID start and end
  geom_vline(xintercept = as.numeric(ymd("2020-03-01")), 
             linetype = "dashed", colour = "orange") +
  geom_vline(xintercept = as.numeric(ymd("2021-06-01")), 
             linetype = "dashed", colour = "orange") +
  geom_line(colour = "purple") +
  labs(
    title = "Type 2 diabetes medications per person across NHS Healthboards",
    subtitle = "January 2020 - December 2021 | Orange dashed lines: COVID period (Mar 2020 - Jun 2021)",
    x = "Month",
    y = "Quantity per head") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 45, hjust=1), legend.position = "bottom", plot.title = element_text(face = "bold")) +
  facet_wrap(~HBname, scales = "free_y")

print(totalmeds_plot)

#MENTION FREED SCALES as there were vast differences in prescription across healthboards

An overall upward trend was seen during COVID and this increase accelerated post COVID. Changes in the trend during the COVID perios may be due to the enforcement and lifting of lockdown rules. To further examine and link trends to deprived areas. I will be using January 2020 (baseline), January 2021 (third lockdown), December 2022 (post pandemic/current trend). - I chose these seasons to balance temporal evidence (pandemic stages) and seasonal consistency (comparing winter months avoids confounding by seasonality)

key_months_data <- combined_health_data %>% 
  filter(paid_date_month %in% as.Date(c("2020-01-01", "2021-12-01"))) %>% 
  group_by(HBname) %>% 
  mutate(covid_phase = case_when(
    paid_date_month == as.Date("2020-01-01") ~ "pre_covid",
    paid_date_month == as.Date("2021-12-01") ~ "post_covid"))

# Summarise by health board and COVID phase
lollipop_data <- key_months_data %>%
  group_by(HBname, covid_phase) %>% 
  summarise(avg_qph = mean(quantity_per_head, na.rm = TRUE),
            avg_simd = mean(SIMD2020v2_Decile, na.rm = TRUE)) %>%
  # Pivot to get Pre and Post columns for connecting lines
  pivot_wider(names_from = covid_phase, values_from = avg_qph) %>%
  mutate(percent_change = round(((post_covid - pre_covid)/pre_covid)*100, 2)) %>% # To calculate percent change and round to 2 d.p
  # Arrange by descending avg_simd
  arrange(desc(avg_simd)) %>%
  ungroup() %>% #before creating a factor so hat HBname can be arranged in order of avg_simd in graph
  # Create factor to preserve ordering in plot
  mutate(HBname = factor(HBname, levels = HBname))

lollipop_graph <- lollipop_data %>% 
  ggplot() +
  geom_segment(aes(x = HBname, xend = HBname, y = pre_covid, yend = post_covid), 
               color = "grey") +
  geom_point(aes(x = HBname, y = pre_covid,
                 text = round(pre_covid)), 
             color = "orange", size = 3) +
  geom_point(aes(x = HBname, y = post_covid,
                 text = round(post_covid)), 
             color = "purple", size = 3) +
  coord_flip() +
  theme_minimal() +
  theme(plot.title = element_text(face = "bold")) +
  xlab("") +
  ylab("Average Quantity Per Head") +
  ggtitle("Change in Quantity Per Head: Pre-COVID vs Post-COVID",
          subtitle = "NHS Health Boards ordered by average SIMD decile (most to least deprived) \n Data from Jan 2020 and Dec 2021")

# Convert to interactive plotly graph
lollipop_interactive <- ggplotly(lollipop_graph, tooltip = "text")

# Display the interactive plot
lollipop_interactive

This shows that Glasgow was the third highestdeprived and has the highest increase in average prescriptions per head. However this is absolute changes for further analysis, the percentage change was calculated.

# load the NHS Health board Shapefile
NHS_healthboards <- suppressMessages(st_read(here("data/Week6_NHS_HealthBoards_2019.shp"), quiet = TRUE)) #reads in file and hides reading in message

#Join spatial data with other health data
summary_map <- NHS_healthboards %>%
  full_join(lollipop_data, by = join_by(HBName == HBname)) %>% 
  rename(HBname = HBName) # Change column name for consistency
# Plot percentage change map
plot_map <- summary_map %>% 
  ggplot() +
  geom_sf(aes(fill = percent_change, text = paste0("Health Board: ", HBname, "\n", round(percent_change,2), "%")), color = "white", size = 0.3) +
  scale_fill_viridis_c(option = "plasma", 
                       name = "% Change in Average Quantity per Head") +
  labs(
    title = "Percentage Change in Average Type 2 Diabetes Prescriptions Per Head \nPre-COVID → Post-COVID",
    caption = "Data from Jan 2020 and Dec 2021"
  ) +
  theme_minimal() +
  theme(legend.position = "right", plot.title = element_text(face = "bold"))

interactive_map <- ggplotly(plot_map, tooltip = "text")

interactive_map
#Add click to see name of healthboard and exact percentage number

This revealed that the Western Isles had the highest percentage increase which as also part of the Top 5 most deprived areas. However other most deprived areas such as Ayrshire and Arran, Greater Glasgow and Clyde had one of the lowest percentage increases. A Summary table was produced to better display and understand the relationship between deprivation, absolute and percentage increase in avrage type 2 diabetes medication per head.

ranked_table <- lollipop_data %>%  # replace with your dataframe name
  arrange(avg_simd) %>% 
  mutate(Rank = row_number(),
         absolute_change = round(post_covid - pre_covid, 2)) %>%
  select(c(Rank, HBname, pre_covid, absolute_change, percent_change))

# ---- Step 2: GT table ----
ranked_table %>%
  gt() %>%
  cols_label(
    Rank = html("Rank<br>(Most to Least Deprived)"),
    HBname = "Health Board",
    pre_covid = "Pre-COVID Level (per head)",
    absolute_change = "Absolute Increase",
    percent_change = "Percentage Increase (%)"
  ) %>%
  fmt_number(
    columns = c(pre_covid),
    decimals = 0) %>%
  tab_header(
    title = " Health Boards by Percentage Increase in Type 2 Diabetes Prescriptions",
    subtitle = "Comparing pre-COVID (Jan 2020) to post-COVID (Dec 2021)"
  ) %>% 
#Colour scale for Absolute Increase
  data_color(
    columns = absolute_change,
    method = "numeric",
    palette = "Oranges"
  ) %>%
  # Colour scale for Percentage Increase
  data_color(
    columns = percent_change,
    method = "numeric",
    palette = "Purples"
  )
Health Boards by Percentage Increase in Type 2 Diabetes Prescriptions
Comparing pre-COVID (Jan 2020) to post-COVID (Dec 2021)
Rank
(Most to Least Deprived)		 Health Board Pre-COVID Level (per head) Absolute Increase Percentage Increase (%)
1 Ayrshire and Arran 1,981 372.28 18.79
2 Lanarkshire 3,035 540.24 17.80
3 Greater Glasgow and Clyde 4,992 960.94 19.25
4 Western Isles 107 31.07 29.00
5 Dumfries and Galloway 700 125.86 17.98
6 Fife 1,524 274.49 18.01
7 Tayside 1,527 321.51 21.06
8 Highland 1,289 302.68 23.48
9 Forth Valley 1,455 302.45 20.79
10 Borders 468 93.69 20.00
11 Orkney 97 18.69 19.25
12 Lothian 2,942 714.20 24.28
13 Shetland 87 12.64 14.44
14 Grampian 2,040 463.12 22.70

4 Discussion

HOVER OVER MAP SHOULD SAY HEALTHBOARD NAME

Questions:

  1. In feedback what do you mean by showing links for reproducibility? Is code enough? Or do we need to show/write how we got to the links? #### Write overall urls

How do I make figures wider so it doesn’t cut off names?

Add ‘NHS’ to Healthboards. Sort out Labels of graphs. Ensure names aren’t cut off g Glasgow.

4.0.1 Limitations of the Study

5 Conclusion

Limitations. Next Steps

6 References

Diabetes in Scotland: a rising tidehttps://www.thelancet.com/journals/landia/article/PIIS2213-8587(20)30124-8/fulltext

Prescriptions in the Community https://www.opendata.nhs.scot/dataset/prescriptions-in-the-community

Scottish Census: https://www.scotlandscensus.gov.uk/webapi/jsf/tableView/tableView.xhtml

Scottish Index of Multiple Deprivation 2020v2 data zone lookup file https://www.gov.scot/collections/scottish-index-of-multiple-deprivation-2020/#supportingdocuments

NHS shapeboard graph